Page 473 - 《软件学报》2024年第6期
P. 473
胡凯 等: 基于端到端深度神经网络和图搜索的 OCT 图像视网膜层边界分割方法 3049
[4] Drexler W, Fujimoto JG. Optical Coherence Tomography: Technology and Applications. Cham: Springer Int’l Publishing, 2015. 3–64.
[doi: 10.1007/978-3-319-06419-2]
[5] Fujimoto J, Swanson E. The development, commercialization, and impact of optical coherence tomography. Investigative Ophthalmology
& Visual Science, 2016, 57(9): OCT1–OCT13. [doi: 10.1167/iovs.16-19963]
[6] Puliafito CA, Hee MR, Lin CP, Reichel E, Schuman JS, Duker JS, Izatt JA, Swanson EA, Fujimoto JG. Imaging of macular diseases with
optical coherence tomography. Ophthalmology, 1995, 102(2): 217–229. [doi: 10.1016/S0161-6420(95)31032-9]
[7] Keane PA, Liakopoulos S, Jivrajka RV, Chang KT, Alasil T, Walsh AC, Sadda SR. Evaluation of optical coherence tomography retinal
thickness parameters for use in clinical trials for neovascular age-related macular degeneration. Investigative Ophthalmology & Visual
Science, 2009, 50(7): 3378–3385. [doi: 10.1167/iovs.08-2728]
[8] Malamos P, Ahlers C, Mylonas G, Schütze C, Deak G, Ritter M, Sacu S, Schmidt-Erfurth U. Evaluation of segmentation procedures
using spectral domain optical coherence tomography in exudative age-related macular degeneration. Retina, 2011, 31(3): 453–463. [doi:
10.1097/IAE.0b013e3181eef031]
[9] Bavinger JC, Dunbar GE, Stem MS, Blachley TS, Kwark L, Farsiu S, Jackson GR, Gardner TW. The effects of diabetic retinopathy and
pan-retinal photocoagulation on photoreceptor cell function as assessed by dark adaptometry. Investigative Ophthalmology & Visual
Science, 2016, 57(1): 208–217. [doi: 10.1167/iovs.15-17281]
[10] Sarhan MH, Nasseri MA, Zapp D, Maier M, Lohmann CP, Navab N, Eslami A. Machine learning techniques for ophthalmic data
processing: A review. IEEE Journal of Biomedical and Health Informatics, 2020, 24(12): 3338–3350. [doi: 10.1109/JBHI.2020.3012134]
[11] Ngo L, Yih G, Ji S, Han JH. A study on automated segmentation of retinal layers in optical coherence tomography images. In: Proc. of
the 4th Int’l Winter Conf. on Brain-computer Interface (BCI). Gangwon: IEEE, 2016. 1–2. [doi: 10.1109/IWW-BCI.2016.7457465]
[12] Ishikawa H, Stein DM, Wollstein G, Beaton S, Fujimoto JG, Schuman JS. Macular segmentation with optical coherence tomography.
Investigative Ophthalmology & Visual Science, 2005, 46(6): 2012–2017. [doi: 10.1167/iovs.04-0335]
[13] He QY, Li ZL, Wang XC, Nan N, Lu Y. Automated retinal layer segmentation based on optical coherence tomographic images. Acta
Optica Sinica, 2016, 36(10): 1011003 (in Chinese with English abstract). [doi: 10.3788/AOS201636.1011003]
[14] Naz S, Akram MU, Khan SA. Automated segmentation of retinal layers from OCT images using structure tensor and kernel
regression+GTDP approach. In: Proc. of the 1st Int’l Conf. on Next Generation Computing Applications (NextComp). Mauritius: IEEE,
2017. 98–102. [doi: 10.1109/NEXTCOMP.2017.8016182]
[15] Liu YH, Carass A, He YF, Antony BJ, Filippatou A, Saidha S, Solomon SD, Calabresi PA, Prince JL. Layer boundary evolution method
for macular OCT layer segmentation. Biomedical Optics Express, 2019, 10(3): 1064–1080. [doi: 10.1364/BOE.10.001064]
[16] El Tanboly A, Ismail M, Switala A, Mahmoud M, Soliman A, Neyer T, Palacio A, Hadayer A, El-Azab M, Schaal S, El-Baz A. A novel
automatic segmentation of healthy and diseased retinal layers from OCT scans. In: Proc. of the 2016 IEEE Int ’l Conf. on Image
Processing (ICIP). Phoenix: IEEE, 2016. 116–120. [doi: 10.1109/ICIP.2016.7532330]
[17] Tian J, Varga B, Somfai GM, Lee WH, Smiddy WE, DeBuc DC. Real-time automatic segmentation of optical coherence tomography
volume data of the macular region. PLoS One, 2015, 10(8): e0133908. [doi: 10.1371/journal.pone.0133908]
[18] Hussain MA, Bhuiyan A, Turpin A, Luu CD, Smith RT, Guymer RH, Kotagiri R. Automatic identification of pathology-distorted retinal
layer boundaries using SD-OCT imaging. IEEE Trans. on Biomedical Engineering, 2017, 64(7): 1638–1649. [doi: 10.1109/TBME.2016.
2619120]
[19] Niu SJ, Chen Q, Lu ST, Shen HL. SD-OCT image layer segmentation using multi-scale 3-D graph search method. Computer Science,
2015, 42(9): 272–277 (in Chinese with English abstract). [doi: 10.11896/j.issn.1002-137X.2015.9.053]
[20] Stankiewicz A, Marciniak T, Dabrowski A, Stopa M, Rakowicz P, Marciniak E. Novel full-automatic approach for segmentation of
epiretinal membrane from 3D OCT images. In: Proc. of the 2017 Signal Processing: Algorithms, Architectures, Arrangements, and
Applications (SPA). Poznan: IEEE, 2017. 100–105. [doi: 10.23919/SPA.2017.8166846]
[21] Vermeer KA, Van der Schoot J, Lemij HG, De Boer JF. Automated segmentation by pixel classification of retinal layers in ophthalmic
OCT images. Biomedical Optics Express, 2011, 2(6): 1743–1756. [doi: 10.1364/BOE.2.001743]
[22] Lang A, Carass A, Hauser M, Sotirchos ES, Calabresi PA, Ying HS, Prince JL. Retinal layer segmentation of macular OCT images using
boundary classification. Biomedical Optics Express, 2013, 4(7): 1133–1152. [doi: 10.1364/BOE.4.001133]
[23] Lang A, Carass A, Bittner AK, Ying HS, Prince JL. Improving graph-based OCT segmentation for severe pathology in Retinitis
Pigmentosa patients. In: Proc. of the 2017 SPIE Medical Imaging Conf. on Biomedical Applications in Molecular, Structural, and
Functional Imaging. Orlando: SPIE, 2017. 101371M. [doi: 10.1117/12.2254849]
[24] Nath SS, Anoop BK, Sankar P. Classification of outer retinal layers based on KNN-classifier. In: Proc. of the 2018 Int ’l Conf. on
Emerging Trends and Innovations in Engineering and Technological Research (ICETIETR). Ernakulam: IEEE, 2018. 1–4. [doi: 10.1109/